Q&A with Duncan Bain, Senior Energy Advisor at SAS

Duncan Bain, Senior Energy Advisor at SAS
Duncan Bain, Senior Energy Advisor at SAS, discusses with Energy Digital the key role data and analytics plays in accurate forecasting for energy companies

There’s no denying the vital role that data and analytics play in the ever-evolving energy landscape. These tools are essential to helping companies navigate complex challenges and seize new opportunities. 

One advocate for the benefit of these dynamics is Duncan Bain, Senior System Engineer at SAS.

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Duncan, who boasts a wealth of experience from his previous role as Head of Data and Insight at ScottishPower — a company he worked for for 15 years — offers unique insights into the intersection of technology and energy. 

In this exclusive sit-down with Energy Digital, Duncan delves into how SAS’s innovative solutions are enabling businesses across the UK, Ireland and Northern Europe to adapt to and thrive in the shifting energy sector

Q. What are some of the most significant challenges energy companies face in accurately forecasting supply and demand within the evolving energy landscape, particularly amid the transition towards renewable sources?

Globally the energy industry is undergoing seismic changes, both in the way it generates and consumes energy. 

We’re moving on from traditional load forecasting, where predictions are made around how much electricity will be needed at a given time and how that demand will affect the utility grid, to more complex multimodal forecasting models by analysing engagement with demand response measures and behind-the-meter technologies. 

On the supply side, we’re seeing an increasing impact from weather dependent generation in wind, solar and hydro and while this is positive from a renewable energy point of view, it’s having a big impact on the sector and its ability to manage the grid. 

The uptake of domestic solar panels, electric vehicles and heat pumps is also creating some forecasting challenges, as well as some households choosing to participate in demand flexibility schemes. The more data we have around this, the more accurate predictions can be made. Energy companies are already using smart meter readings and general demographic data to understand when network reinforcement might need to take place. 

However, a global challenge in retrieving smart meter data does still exist. In the UK, suppliers are responsible for the meter whereas in Europe these are managed directly by the network. This brings into question data and privacy laws and the industry is still working through these challenges to ensure all parties can get the data they need whilst ensuring individuals rights are protected. 

Q. What is your understanding of the critical role data and analytics play in mitigating risks associated with grid management and ensuring a smooth energy transition?

I’m passionate about the fact that the energy transition needs to be fair and just in order to positively impact as many people as possible. 

To achieve this data and analytics are critical to enabling smarter decisions and improving knowledge of both new and existing systems. A McKinsey paper suggested that the global spend required on energy infrastructure is US$9.2 trillion annually between now and 2050 — if we are to meet internationally agreed climate targets. 

However, that investment needs to be carefully targeted to ensure no customer is left behind.

There’s no denying that the energy transition is complex. Working directly in the industry has allowed me access to key insights and to see first-hand the intricacy behind something which most consumers wouldn't even think of, such as the effects of turning on a light. 

Yes, there is physical work which needs to be done to transform the energy system but as with all large-scale infrastructure projects, the hardest part is people’s capacity to change. It’s vitally important we understand their ability to participate in the change and put in place the right measures to enable them to do so — and the only way to do this effectively is with accurate insight around usage patterns, consumption levels and their requirements of the network. 

Q. Could you provide examples where data-driven insights have enabled energy companies to better anticipate and respond to fluctuations in supply and demand, thereby improving grid stability and overall operational efficiency?

SAS has customers across the globe who are using our platforms and solutions to deliver insights with a direct impact on day-to-day operations of the company. For example, Repsol relies on SAS Energy Forecasting to play a crucial role in optimising energy usage, having deployed SAS software across three of its main business lines: energy management, analytics and predictions. 

Wienerberger, a prosumer and one of Europe’s largest brick manufacturers, uses SAS Energy Cost Optimisation to optimise energy usage resulting in a reduction in their energy footprint by up to 15%. Enel Green Power are using SAS Analytics for IoT to monitor their fleet of wind turbines and have reduced the time to complete Wind Power Generation Unit analysis from one month to two days. 

Finally, at SAS we use our own technology to help monitor our solar farm at our global headquarters in Cary, North Carolina, using advanced AI to determine what output we should expect in any given weather condition and drive decision making around when physical intervention is needed to repair or replace panels. 


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